Fast and Accurate Goal-Directed Motion Synthesis For Crowds

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Fast and Accurate Goal-Directed Motion Synthesis
For Crowds

• Mankyu Sung
• Lucas Kovar
• Michael Gleicher
• University of Wisconsin- Madison
• www.cs.wisc.edu/graphics

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The Goal Motion synthesis for crowds
High-level behaviors (Musse 2001, Ucliney 2002,
Faranc 1990, Sung 2004, Braun 2003)
Low-level motion synthesis
Our goal
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The GoalMotion synthesis for crowds
Orientation

• Problem Constrained motion synthesis
• Positions, Orientation, Poses, Time duration
• Requirement
• Fast performance
• Accurate meeting constraints
• High quality motions
• Collision avoidance
• Complicated environment

Pose
Position
Target
Time duration
Initial
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An example
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Our approach

• Synthesize crowds one individual at a time

• Motion graphs for low-level synthesis
• (Kovar et al. 02, Lee et al. 02, Arikan and
  Forsyth 02, Gleicher et al. 03)

• Must adapt to crowds
• Individual motions must be found very quickly
• Pure discrete synthesis cannot meet continuous
  constraints

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Adapting Graph based synthesis

• Two-level synthesis
• Coarse search for global path planning
• Finer search for detailed motion synthesis
• Quickly find long motions in complex environments
• Incorporate continuous motion adjustment
• Discrete search to roughly satisfy constraints
• Additional displacements for precision
• Improves speed and accuracy

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Contents

• Related work
• Synthesis Algorithms
• Demos
• Limitation

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Related Work (1)

• Graph based motion synthesis (e.g. Arikan
  2002, Arikan 2003, Gleicher 2003, Kovar 2002, Hue
  2004, Lee 2002, Lee 2004, Reitsma 2004)
• Connecting discrete finite clips with simple
  interpolation or displacement mapping

-Create new motion strictly by attaching clips
? Hard to satisfy constraints exactly - Do not
consider crowds.
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Related Work (2)

• Planning Biped Locomotion (Choi 2003)
• Build a PRM (Probability Roadmap Method) based on
  sampled footprints configurations.
• Given initial and target constraint, the PRM is
  searched to find a path that is able to connect
  with motion clips.
• Motions are adjusted to meet the constraints.

-The PRM is tightly coupled with motion clips
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Related Work (3)

• Procedural motion synthesis (Bouvier 1997, Boulic
  1990, Sun 2001, Boulic 2004)
• Controllable but not as realistic as motion
  capture data
• Motion Blending (Guo 1996, Park 2004, Petteré
  2003)
• Continuous control over trajectory
• Limited and computationally costly
• Crowd Modeling (Musse 2001, Ulicny 2002, Farenc
  1999)
• Focus on high-level behaviors
• Not have constraints to satisfy

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Algorithm

• Example

• Rough planning
• PRM query
• Fine planning
• Greedy search
• Create seed paths
• If distance gt e
• Randomly select and replace a clip
• Joining with adjustment

Target
Obstacle
Initial
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Algorithm

• Example

• Rough planning
• PRM query
• Fine planning
• Greedy search
• Create seed motions
• If distance gt e
• Randomly select and replace a clip
• Joining with adjustment

Target
Obstacle
Initial
waypoints
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Algorithm

• Example

• Rough planning
• PRM query
• Fine planning
• Greedy search
• Create seed motions
• If distance gt e
• Randomly select and replace a clip
• Joining with adjustment

Target
Obstacle
Initial
1
2
3
waypoints
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Algorithm

• Rough planning
• PRM query
• Fine planning
• Greedy search
• Create seed motions
• If distance gt e
• Randomly select and replace a clip
• Joining with adjustment

• Example

Target
Obstacle
Forward Motion(Mf)
Initial
Backward Motion(Mb)
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2
3
Initial
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Algorithm
Cost function How close are they? C(Mf, Mb)

• Rough planning
• PRM query
• Fine planning
• Greedy search
• Create seed motions
• If distance gt e
• Randomly select and replace a clip
• Joining with adjustment

gt e
Forward motions
Backward motions
Compare all pair of motions and returns minimum
cost
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Algorithm

• Rough planning
• PRM query
• Fine planning
• Greedy search
• Create seed motions
• If distance gt e
• Randomly select and replace a clip
• Joining with adjustment

lt e
New motions
Old Motions
Old Motionsc
Random select and Replace a clip
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Algorithm

• Rough planning
• PRM query
• Fine planning
• Greedy search
• Create seed paths
• If distance gt e
• Randomly select and replace a clip
• Joining with adjustment

• Example

Target
Obstacle
Initial
Joining
waypoints
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Motion adjustment
Old Motions
New motions
New motions
Old Motions
e
The error is distributed to the both paths
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Demos

• Time constrained demo
• A theater
• Box delivery
• Big crowds on virtual environment

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Performance results
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Performance results
Speed vs. avg. distance between
characters
Speed vs. e
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Limitation

• Not optimal
• May cause some wandering effect
• Offline
• Need searching time
• Performance depends on environment
• Density of crowds affects on performance
• The environment (size and complexity) does matter

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Acknowledgement

• Financial support NSF CCR-9984506 and
  CCR-0204372
• Motion donations House of Moves
• Hyun Joon Shin for STM system